HLMTrans: A Sim-to-Real Transfer Framework for Spatial Crowdsourcing with Human-Guided Language Models

Abstract

Reinforcement Learning (RL), trained via trial and error in simulators, has been proven to be an effective approach for addressing task assignment problems in spatial crowdsourcing. However, a performance gap still exists when transferring the simulator-trained RL Models (RLMs) to real-world settings due to the misalignment of travel time. Existing works mostly focus on using data-driven and learning-based methods to predict travel time; unfortunately, these approaches are limited in achieving accurate predictions by requiring a large amount of real-world data covering the entire state distribution. In this paper, we propose a Sim-to-Real Transfer with Human-guided Language Models framework called HLMTrans, which comprises three core modules: RLMs decision for task assignment, sim-to-real transfer with Large Language Models (LLMs), and preference learning from human feedback. HLMTrans first leverages the zero-shot chain-of-thought reasoning capability of LLMs to estimate travel time by capturing the real-world dynamics. This estimation is then input as domain knowledge into the forward model of Grounded Action Transformation (GAT) to enhance the action transformation of RLMs. Further, we design a human preference learning mechanism to fine-tune LLMs, improving their generation quality and enabling RLMs learn a more realistic policy. We evaluate the proposed HLMTrans on two real-world datasets, and the experimental results demonstrate that HLMTrans outperforms the SOTA methods in terms of effectiveness and efficiency.